code: def __init__(self, row_offsets, column_indices, values, shape):
      assert row_offsets.ndim == 1
      assert column_indices.ndim == 1
      assert values.ndim == 2
  
      self.__row_offsets = row_offsets.contiguous()
      self.__row_indices = _diffsort(row_offsets).to(row_offsets.dtype)
      self.__column_indices = column_indices.contiguous()
      self.__values = values.contiguous()
  
      self.__transp_info = _get_transpose_info(
          self.shape[1],
          self.shape[2],
          self.__row_indices,
          self.__row_offsets,
          self.__column_indices,
      )
  
imports: import torch
  from xformers.ops import masked_matmul
  from xformers.sparse import _csr_ops
  from xformers.sparse.utils import _csr_to_coo, _dense3d_to_sparse, _diffsort, _get_transpose_info, _transpose_with_info
label_desc: Use image node_id 2 to create a new SparseCSRTensor object from inherited base classes: torch with example: obj = SparseCSRTensor(row_offsets, column_indices, values, shape)